为什么OpenCV Gpu模块的性能要比VisionWorks快? [英] Why is OpenCV Gpu module performing faster than VisionWorks?
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问题描述
我尝试了OpenCv gpu模块的几种功能，并将相同的行为与visionWorks立即代码进行了比较.令人惊讶的是，在所有情况下，OpenCv Gpu模块的执行速度都比VisionWorks显着快.
I have tried several functions of OpenCv gpu module and compared the same behavior with visionWorks immediate code. And surprisingly, it all circumstances the OpenCv Gpu Module is performing significantly faster than VisionWorks.
e-g 使用opencv
e-g a Gaussian pyramid of level 4 implemented manually using opencv
#include <iostream>
#include <stdio.h>
#include <stdio.h>
#include <queue>
/* OPENCV RELATED */
#include <cv.h>
#include <highgui.h>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/stitching/detail/util.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include <opencv2/gpu/gpu.hpp>
#include "opencv2/opencv_modules.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/util.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
using namespace gpu;
using namespace cv::detail;
int main()
{
Mat m = imread("br1.png");
GpuMat d_m = GpuMat (m);
GpuMat d_m2;
GpuMat l1,l2,l3,l4;
int iter = 100;
int64 e = getTickCount();
float sum = 0;
sum = 0;
for(int i = 0 ; i < iter; i++)
{
e = getTickCount();
gpu::pyrDown(d_m,l1);
gpu::pyrDown(l1,l2);
gpu::pyrDown(l2,l3);
gpu::pyrDown(l3,l4);
sum+= (getTickCount() - e) / getTickFrequency();
}
cout <<"Time taken by Gussian Pyramid Level 4 \t\t\t"<<sum/iter<<" sec"<<endl;
//imwrite("cv_res.jpg",res);
return 0;
}
平均花费2.5毫秒进行100次迭代.而VisionWorks
takes 2.5 ms on average for 100 iterations. Whereas, VisionWorks
#include <VX/vx.h>
#include <VX/vxu.h>
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <stdio.h>
#include <stdio.h>
#include <queue>
/* OPENCV RELATED */
#include <cv.h>
#include <highgui.h>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/stitching/detail/util.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include <opencv2/gpu/gpu.hpp>
#include "opencv2/opencv_modules.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/util.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
using namespace gpu;
using namespace cv::detail;
vx_image createImageFromMat(vx_context& context, cv::Mat& mat);
vx_status createMatFromImage(vx_image& image, cv::Mat& mat);
/* Entry point. */
int main(int argc,char* argv[])
{
Mat cv_src1 = imread("br1.png", IMREAD_GRAYSCALE);
int width = 1280;
int height = 720;
int half_width = width/2;
int half_height = height/2;
Mat dstMat(cv_src1.size(), cv_src1.type());
Mat half_dstMat(Size(width/16,height/16),cv_src1.type());
/* Image data. */
if (cv_src1.empty() )
{
std::cerr << "Can't load input images" << std::endl;
return -1;
}
/* Create our context. */
vx_context context = vxCreateContext();
/* Image to process. */
vx_image image = createImageFromMat(context, cv_src1);
//NVXIO_CHECK_REFERENCE(image);
/* Intermediate images. */
vx_image dx = vxCreateImage(context, width, height, VX_DF_IMAGE_S16);
vx_image dy = vxCreateImage(context, width, height, VX_DF_IMAGE_S16);
vx_image mag = vxCreateImage(context, width, height, VX_DF_IMAGE_S16);
vx_image half_image = vxCreateImage(context, half_width, half_height, VX_DF_IMAGE_U8);
vx_image half_image_2 = vxCreateImage(context, half_width/2, half_height/2, VX_DF_IMAGE_U8);
vx_image half_image_3 = vxCreateImage(context, half_width/4, half_height/4, VX_DF_IMAGE_U8);
vx_image half_image_4 = vxCreateImage(context, half_width/8, half_height/8, VX_DF_IMAGE_U8);
int64 e = getTickCount();
int iter = 100;
float sum = 0.0;
e = getTickCount();
iter = 100;
for(int i = 0 ; i < iter; i ++)
{
/* RESIZEZ OPERATION */
if(vxuHalfScaleGaussian(context,image,half_image,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image,half_image_2,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image_2,half_image_3,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image_3,half_image_4,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
sum += (getTickCount() - e) / getTickFrequency();
}
cout <<"Resize to half " <<sum/iter<<endl;
createMatFromImage(half_image_4,half_dstMat);
imwrite("RES.jpg",half_dstMat);
/* Tidy up. */
vxReleaseImage(&dx);
vxReleaseImage(&dy);
vxReleaseImage(&mag);
vxReleaseContext(&context);
}
vx_image createImageFromMat(vx_context& context, cv::Mat& mat)
{
vx_imagepatch_addressing_t src_addr = {
mat.cols, mat.rows, sizeof(vx_uint8), mat.cols * sizeof(vx_uint8), VX_SCALE_UNITY, VX_SCALE_UNITY, 1, 1 };
void* src_ptr = mat.data;
vx_image image = vxCreateImageFromHandle(context, VX_DF_IMAGE_U8, &src_addr, &src_ptr, VX_IMPORT_TYPE_HOST);
return image;
}
vx_status createMatFromImage(vx_image& image, cv::Mat& mat)
{
vx_status status = VX_SUCCESS;
vx_uint8 *ptr = NULL;
cout <<"Creating image "<<mat.cols << " " <<mat.rows <<endl;
vx_rectangle_t rect;
vxGetValidRegionImage(image, &rect);
vx_imagepatch_addressing_t addr = {
mat.cols, mat.rows, sizeof(vx_uint8), mat.cols * sizeof(vx_uint8), VX_SCALE_UNITY, VX_SCALE_UNITY, 1, 1 };
status = vxAccessImagePatch(image, &rect, 0, &addr, (void **)&ptr, VX_READ_ONLY);
mat.data = ptr;
return status;
}
单次执行需要11.1毫秒，而100次迭代平均需要96毫秒.
takes 11.1 ms on single execution, and 96ms on average for 100 iterations.
如果这通常是正确的，那么visionWorks提供了什么?
If this is generally true, then what does visionWorks offer ?
我正在Jetson TK1上运行L4T的"cuda-repo-l4t-r21.3-6-5-local_6.5-50"版本
I am running "cuda-repo-l4t-r21.3-6-5-local_6.5-50" version of L4T on Jetson TK1
推荐答案
您在VisionWorks代码中犯了一个错误.您只能在循环之前e = getTickCount();启动一次计时器，但是您需要在每次迭代时启动它.
You've made a mistake in VisionWorks code. You start timer only once e = getTickCount(); right before the loop, but you need to start it on each iteration.
iter = 100;
for(int i = 0 ; i < iter; i ++)
{
// START TIMER
e = getTickCount();
/* RESIZEZ OPERATION */
if(vxuHalfScaleGaussian(context,image,half_image,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image,half_image_2,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image_2,half_image_3,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
if(vxuHalfScaleGaussian(context,half_image_3,half_image_4,3) != VX_SUCCESS)
{
cout <<"ERROR :"<<"failed to perform scaling"<<endl;
}
// STOP TIMER
sum += (getTickCount() - e) / getTickFrequency();
}
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